Energy metabolism failure in proximal tubule cells (PTCs) is a hallmark of chronic kidney injury. We combined transcriptomic, metabolomic, and lipidomic approaches in experimental models and patient ...cohorts to investigate the molecular basis of the progression to chronic kidney allograft injury initiated by ischemia/reperfusion injury (IRI). The urinary metabolome of kidney transplant recipients with chronic allograft injury and who experienced severe IRI was substantially enriched with long chain fatty acids (FAs). We identified a renal FA-related gene signature with low levels of carnitine palmitoyltransferase 2 (Cpt2) and acyl-CoA synthetase medium chain family member 5 (Acsm5) and high levels of acyl-CoA synthetase long chain family member 4 and 5 (Acsl4 and Acsl5) associated with IRI, transition to chronic injury, and established chronic kidney disease in mouse models and kidney transplant recipients. The findings were consistent with the presence of Cpt2
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PTCs failing to recover from IRI as identified by single-nucleus RNA-Seq. In vitro experiments indicated that ER stress contributed to CPT2 repression, which, in turn, promoted lipids’ accumulation, drove profibrogenic epithelial phenotypic changes, and activated the unfolded protein response. ER stress through CPT2 inhibition and lipid accumulation engaged an auto-amplification loop leading to lipotoxicity and self-sustained cellular stress. Thus, IRI imprints a persistent FA metabolism disturbance in the proximal tubule, sustaining the progression to chronic kidney allograft injury.
Nonlocality is a fundamental trait of quantum many-body systems, both at the level of pure states, as well as at the level of mixed states. Because of nonlocality, mixed states of any two subsystems ...are correlated in a stronger way than what can be accounted for by considering the correlated probabilities of occupying some microstates. In the case of equilibrium mixed states, we explicitly build two-point quantum correlation functions, which capture the specific, superior correlations of quantum systems at finite temperature, and which are directly accessible to experiments when correlating measurable properties. When nonvanishing, these correlation functions rule out a precise form of separability of the equilibrium state. In particular, we show numerically that quantum correlation functions generically exhibit a finite quantum coherence length, dictating the characteristic distance over which degrees of freedom cannot be considered as separable. This coherence length is completely disconnected from the correlation length of the system-as it remains finite even when the correlation length of the system diverges at finite temperature-and it unveils the unique spatial structure of quantum correlations.
The application of single-cell technologies in clinical nephrology remains elusive. We generated an atlas of transcriptionally defined cell types and cell states of human kidney disease by ...integrating single-cell signatures reported in the literature with newly generated signatures obtained from 5 patients with acute kidney injury. We used this information to develop kidney-specific cell-level information ExtractoR (K-CLIER), a transfer learning approach specifically tailored to evaluate the role of cell types/states on bulk RNAseq data. We validated the K-CLIER as a reliable computational framework to obtain a dimensionality reduction and to link clinical data with single-cell signatures. By applying K-CLIER on cohorts of patients with different kidney diseases, we identified the most relevant cell types associated with fibrosis and disease progression. This analysis highlighted the central role of altered proximal tubule cells in chronic kidney disease. Our study introduces a new strategy to exploit the power of single-cell technologies toward clinical applications.
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•Single-cell atlas of transcriptionally defined cell types/states of kidney disease•A transfer learning model for dimensionality reduction applicable to small datasets•Computational model to link clinical data to kidney cell states using bulk RNAseq•Evidence for a central role of altered proximal tubule cells in kidney fibrosis
Cell biology; Integrative aspects of cell biology; Transcriptomics; Machine learning
We introduce a new numerical technique, the bosonic auxiliary-field Monte Carlo method, which allows us to calculate the thermal properties of large lattice-boson systems within a systematically ...improvable semiclassical approach, and which is virtually applicable to any bosonic model. Our method amounts to a decomposition of the lattice into clusters, and to an ansatz for the density matrix of the system in the form of a cluster-separable state-with nonentangled, yet classically correlated clusters. This approximation eliminates any sign problem, and can be systematically improved upon by using clusters of growing size. Extrapolation in the cluster size allows us to reproduce numerically exact results for the superfluid transition of hard-core bosons on the square lattice, and to provide a solid quantitative prediction for the superfluid and chiral transition of hardcore bosons on the frustrated triangular lattice.
Abstract
Introduction
Perinatal depression (PND) is a complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying predisposing factors for PND ...during early pregnancy is key to early detection of women who may be at greater risk of developing this condition. Machine Learning (ML) techniques have recently been applied in a few studies, mostly to predict postpartum depression. None of them, however, considered sleep data in model building, neither focused on PND.
Methods
By analyzing data from a multicenter, prospective cohort study on sleep and mood changes during the perinatal period (the “Life-ON” study), we constructed a ML model for PND risk prediction and tested it in a cross-validation setting. PND was assessed using an EPDS score >12 during 9 visits from early pregnancy until 6 months postpartum. In a bivariate analysis, 47 sociodemographic, psychological, blood-based, medical/gynecological, and subjective sleep variables, collected from 439 pregnant women (33.7±4.2 yrs.) during the first trimester of gestation, as well as 33 polysomnographic (PSG) parameters, recorded from 353 women (33.6±4.2 yrs.) during the second trimester, were correlated with PND. A support vector machine (SVM) model was then trained using the 10-features with the highest permutation importance to predict the individual risk of PND for each woman.
Results
Among all variables considered, sleep quality (PSQI) and insomnia symptoms (ISI) (p< 0.001), daytime sleepiness (ESS) and RLS severity (IRLS) (p< 0.05), as well as the PSG parameters Apnea Index (p=0.001), number of central hypopneas and percentage of sleep stage N2 (p< 0.05), were all positively correlated to PND. The PSQI and ISI scores were also selected by the SVM classifier, which achieved a mean AUROC of 0.777 and an AUPRC of 0.393, corresponding to a sensitivity of 54.3% and a specificity of 82.6% in identifying women at risk for PND.
Conclusion
In our data-driven ML model to predict the risk of PND during early pregnancy, subjective poor sleep quality and insomnia symptoms identified women at greater risk of developing PND, while none of the PSG variables improved model performance.
Support (if any)
Swiss National Science Foundation (grant: 320030_160250/1). The Italian Ministry of Health and Emilia-Romagna Region (grant: PE-2011-02348727).
Abstract
Background and Aims
Understanding the cell-intrinsic mechanisms contributing to the maintenance of a dysfunctional cellular state in chronic kidney disease (CKD) and identifying therapeutic ...targets are research priorities in renal medicine. A key contributor to chronic kidney histological damage is acute kidney injury, especially ischemia-reperfusion injury (IRI). Persistent cell-extrinsic perturbations generated upon IRI, for example hypoxia, impair the energetic metabolism of Proximal Tubular Cells (PTC) participating in the process of transition from acute to chronic kidney injury. Here, we propose to investigate the PTC intrinsic factors involved in the perpetuation of an impaired cellular state which contribute to CKD progression
Method
We combined single nucleus transcriptomic, metabolomic and lipidomic approaches in experimental models and patient cohorts to investigate the molecular bases of the progression to chronic kidney allograft injury initiated by IRI.
Results
The urinary metabolome of kidney transplant recipients with chronic allograft injury and who experienced severe IRI was significantly enriched with long chain fatty acids (FA). We identified a renal FA-related gene signature with low levels of Cpt2 and Acsm5 and high levels of Acsl4 and Acsm5 associated with IRI, transition to chronic injury, and established CKD in mouse models and kidney transplant recipients. The findings were consistent with the presence of Cpt2-, Acsl4+, Acsl5+, Acsm5- PTC failing to recover from IRI as identified by single nucleus RNA sequencing. In vitro experiments indicated that endoplasmic reticulum (ER) stress contributes to CPT2 repression, which, in turn, promotes lipids accumulation, drives profibrogenic epithelial phenotypic changes, and activates the unfolded protein response.
Conclusion
ER stress through CPT2 inhibition and lipid accumulation, engages an auto-amplification loop leading to lipotoxicity and self-sustained cellular stress. Thus, IRI imprints a persistent FA metabolism disturbance in the proximal tubule sustaining the progression to chronic kidney allograft injury.
The mean-field approximation is at the heart of our understanding of complex systems, despite its fundamental limitation of completely neglecting correlations between the elementary constituents. In ...a recent work Phys. Rev. Lett. 117, 130401 (2016), we have shown that in quantum many-body systems at finite temperature, two-point correlations can be formally separated into a thermal part and a quantum part and that quantum correlations are generically found to decay exponentially at finite temperature, with a characteristic, temperature-dependent quantum coherence length. The existence of these two different forms of correlation in quantum many-body systems suggests the possibility of formulating an approximation, which affects quantum correlations only, without preventing the correct description of classical fluctuations at all length scales. Focusing on lattice boson and quantum Ising models, we make use of the path-integral formulation of quantum statistical mechanics to introduce such an approximation, which we dub quantum mean-field (QMF) approach, and which can be readily generalized to a cluster form (cluster QMF or cQMF). The cQMF approximation reduces to cluster mean-field theory at T=0, while at any finite temperature it produces a family of systematically improved, semi-classical approximations to the quantum statistical mechanics of the lattice theory at hand. Contrary to standard MF approximations, the correct nature of thermal critical phenomena is captured by any cluster size. In the two exemplary cases of the two-dimensional quantum Ising model and of two-dimensional quantum rotors, we study systematically the convergence of the cQMF approximation towards the exact result, and show that the convergence is typically linear or sublinear in the boundary-to-bulk ratio of the clusters as T→0, while it becomes faster than linear as T grows. These results pave the way towards the development of semiclassical numerical approaches based on an approximate, yet systematically improved account of quantum correlations.
•Perinatal depression (PND) is a highly prevalent complication of pregnancy.•It is difficult to predict which women will experience depression during the peripartum.•Machine learning techniques may ...help identifying predictors of PND during early pregnancy.•We developed a data-driven ML model to quantify the risk of developing PND symptoms.•Besides psychosocial factors, sleep alterations were found to be a strong predictor of PND.
Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this condition. We applied a machine learning (ML) approach to data from a multicenter cohort study on sleep and mood changes during the perinatal period (“Life-ON”) to derive models for PND risk prediction in a cross-validation setting. A wide range of sociodemographic variables, blood-based biomarkers, sleep, medical, and psychological data collected from 439 pregnant women, as well as polysomnographic parameters recorded from 353 women, were considered for model building. These covariates were correlated with the risk of future depression, as assessed by regularly administering the Edinburgh Postnatal Depression Scale across the perinatal period. The ML model indicated the mood status of pregnant women in the first trimester, previous depressive episodes and marital status, as the most important predictors of PND. Sleep quality, insomnia symptoms, age, previous miscarriages, and stressful life events also added to the model performance. Besides other predictors, sleep changes during early pregnancy should therefore assessed to identify women at higher risk of PND and support them with appropriate therapeutic strategies.
This study aimed to assess the concordance of various psychometric scales in detecting Perinatal Depression (PND) risk and diagnosis. A cohort of 432 women was assessed at 10-15th and 23-25th ...gestational weeks, 33-40 days and 180-195 days after delivery using the Edinburgh Postnatal Depression Scale (EPDS), Visual Analogue Scale (VAS), Hamilton Depression Rating Scale (HDRS), Montgomery-Åsberg Depression Rating Scale (MADRS), and Mini International Neuropsychiatric Interview (MINI). Spearman's rank correlation coefficient was used to assess agreement across instruments, and multivariable classification models were developed to predict the values of a binary scale using the other scales. Moderate agreement was shown between the EPDS and VAS and between the HDRS and MADRS throughout the perinatal period. However, agreement between the EPDS and HDRS decreased postpartum. A well-performing model for the estimation of current depression risk (EPDS > 9) was obtained with the VAS and MADRS, and a less robust one for the estimation of current major depressive episode (MDE) diagnosis (MINI) with the VAS and HDRS. When the EPDS is not feasible, the VAS may be used for rapid and comprehensive postpartum screening with reliability. However, a thorough structured interview or clinical examination remains necessary to diagnose a MDE.
Les atomes froids dans les réseaux optiques permettent d'avoir un contrôle sans précédent des états a N-corps fortement corrélés. Pour cette raison, ils représentent un excellent outil pour ...l'implémentation d'un « simulateur quantique », utile pour réaliser de manière expérimentale de nombreux hamiltoniens de systèmes d'intérêt physique. En particulier, ils rendent possible la création de champs de jauge artificiels; ces derniers permettant d'accéder à la physique du magnétisme frustré. Dans ce travail, il s'agit de s'intéresser à la thermodynamique des atomes froids, en abordant ce sujet de manière théorique et numérique. A ce jour, le Monte Carlo quantique est la méthode la plus efficace dans ce domaine. Néanmoins, en raison de ce qu'on appelle le « problème du signe », elle ne peut s'appliquer qu'à une classe restreinte de systèmes, et dont par exemple les systèmes frustrés ne font pas partie. L'intérêt de cette thèse est de développer une nouvelle méthode approximée fondée sur une approche Monte Carlo. La première partie de cette thèse est consacrée à des considérations de nature théorique sur la structure spatiale des corrélations classiques et quantiques. Ces résultats nous permettent de développer, dans une deuxième partie, une approximation nommée « champ moyen quantique ». Celle-ci permet de proposer, dans une troisième partie, une méthode numérique qu'on appelle « Monte Carlo du champ auxiliaire » et qu'on applique à des cas d'intérêt physique, notamment au réseau triangulaire frustré.
Cold atoms in optical lattices offer unprecedented control over strongly correlatedmany-body states. For this reason they represent an excellent tool for the implementation ofa “quantum simulator”, which can be used to realize experimentally several Hamiltonians ofsystems of physical interest. In particular, they enable the engineering of artificial gaugefields, which gives access to the physics of frustrated magnetism. In this work, we study thethermodynamics of cold atoms both from a theoretical and a numerical point of view. Atpresent days, the most effective method used in this field is the quantum Monte Carlo. Butbecause of the so-called “sign problem” it can only be applied to a limited class of systems,which for example do not include frustrated systems. The interest of this thesis is to developof a new approximated method based on a Monte Carlo approach. The first part of this workis dedicated to theoretical considerations concerning the spatial structure of quantum andclassical correlations. These results permit to develop, in the second part, an approximationcalled quantum mean-field. This latter allows to propose, in the third part, a numericalmethod that we call “auxiliary-field Monte Carlo” and that we apply to some systems ofphysical interest, among which the frustrated triangular lattice.
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